An overview of Self-Supervised Image Denoising

An overview of Self-Supervised Image Denoising | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An overview of Self-Supervised Image Denoising Dan Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5000884/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The advent of deep learning has brought a revolutionary transformation to image denoising techniques. The persistent challenge of acquiring noisy-clean pairs for supervised methods in real-world scenarios remains formidable, necessitating the exploration of more practical self-supervised image denoising. However, although a large number of reviews on deep learning image denoising methods have emerged in recent years, there is no review specifically on self-supervised image denoising methods. This paper focuses on self-supervised image denoising methods that offer effective solutions to address this challenge. Our comprehensive review analyzes the latest advancements in self-supervised image denoising approaches, categorizing them into three distinct classes: General methods, Blind Spot Network (BSN)-based methods, and Transformer-based methods. For each class, we provide a concise theoretical analysis along with their practical applications. To assess the effectiveness of these methods, we present both quantitative and qualitative experimental results on various datasets. Additionally, we critically discuss the current limitations of these methods and propose promising directions for future research. By offering a detailed overview of recent developments in self-supervised image denoising, this review serves as an invaluable resource for researchers and practitioners in the field, facilitating a deeper understanding of this emerging domain and inspiring further advancements. Artificial Intelligence and Machine Learning Self-supervised unpaired noisy-clean image denoising Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5000884","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":347100817,"identity":"c7450dcd-e896-41cd-9196-97a85c0efccb","order_by":0,"name":"Dan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYDACCRDBBsTMBxuApA0PP38DsVrYEkFK02QkZxwgWksCiHXYxqAhAb8O+dnNzx5+KbPLY2BjbnxcUHOex4DhAOOHjzm4tTDOOWZuLHMuuZiBjbHZeMax2zzmzA3MkjO34dbCLJFgJi3ZxpzYIN/YJs3DdpvHsuEAGzMvHi1sEunfgFrqExvYGIFa/p3jMTiQgF8Lj0SOmeTHtsMQLbxtBwhrkZDIKZNmOHccpKXZmLcvmUdyxsFmvH6Rn5G+TfJHWTVQC/vDxzzf7Oz5+ZsPfviIRws4CHiAhP0BOJ+xAb96kJIfBJWMglEwCkbBiAYAKbpKi5wiJg4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-5625-8615","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Dan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-08-30 03:36:31","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5000884/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5000884/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63746056,"identity":"1504ebf1-d124-417c-9c4f-c2b60bfd6c76","added_by":"auto","created_at":"2024-09-02 02:09:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3041175,"visible":true,"origin":"","legend":"","description":"","filename":"0826.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5000884/v1_covered_db2fe1b5-7408-4dd8-8bf3-8c394528981a.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAn overview of Self-Supervised Image Denoising\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Self-supervised, unpaired, noisy-clean, image denoising","lastPublishedDoi":"10.21203/rs.3.rs-5000884/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5000884/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe advent of deep learning has brought a revolutionary transformation to image denoising techniques. 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